Hydrodynamic optimization of multi-environment reactors for biological nutrient removal: A methodology combining computational fluid dynamics and dimensionless indexes

被引：2

作者：

Blanco-Aguilera R. ^{[1
]}

Lara J.L. ^{[2
]}

Barajas G. ^{[2
]}

Tejero I. ^{[1
]}

Díez-Montero R. ^{[1
,3
]}

机构：

[1] Group of Environmental Engineering, Department of Water and Environmental Sciences and Technologies, University of Cantabria, Avenida los Castros s/n, Santander

[2] Environmental Hydraulics Institute (IHCantabria), University of Cantabria, Isabel Torres 15, Santander

[3] Group of Environmental Engineering and Microbiology, Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya, c/Jordi Girona 1-3, Building D1, Barcelona

来源：

Chemical Engineering Science | 2020年 / 224卷

关键词：

Biological nutrient removal; Computational fluid dynamics; Dimensional analysis; Multi-environment; OpenFOAM®; Optimization;

D O I：

10.1016/j.ces.2020.115766

中图分类号：

学科分类号：

摘要：

Multi-environment reactors are an innovative alternative to simplify conventional Biological Nutrient Removal (BNR) treatment trains as they are more compact and can adapt to existing quality requirements. However, maintaining the desired environmental conditions in different zones of the reactor implies the need for deflectors or mixing devices that generate a complex hydrodynamic behaviour. Therefore, to ensure the desired biological efficiency, hydraulic optimization is essential. For that purpose, a hydrodynamic optimization methodology combining Computational Fluid Dynamics (CFD) and dimensional analysis is developed and presented in this work. The methodology is applied to AnoxAn, an anaerobic-anoxic reactor for BNR. The CFD model is constructed using the OpenFOAM® open source toolbox and has been already validated in a previous work by the authors. Different features as hydraulic separation, dead volumes, short-circuiting or mixing performance are evaluated and main results show that configurations of AnoxAn with high slenderness have the most efficient hydrodynamic behaviour. © 2020 Elsevier Ltd

引用

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